{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:35:07Z","timestamp":1784633707461,"version":"3.55.0"},"reference-count":18,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1109\/globalsip.2018.8646356","type":"proceedings-article","created":{"date-parts":[[2019,3,18]],"date-time":"2019-03-18T20:33:06Z","timestamp":1552941186000},"page":"1159-1163","source":"Crossref","is-referenced-by-count":10,"title":["ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM"],"prefix":"10.1109","author":[{"given":"Tsui-Wei","family":"Weng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pin-Yu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurelie","family":"Lozano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cho-Jui","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Daniel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","first-page":"97","article-title":"Reluplex: An efficient smt solver for verifying deep neural networks","author":"katz","year":"2017","journal-title":"International Conference on Computer Aided Verification"},{"key":"ref11","article-title":"Towards fast computation of certified robustness for relu networks","author":"weng","year":"2018","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref12","first-page":"2263","article-title":"Formal guarantees on the robustness of a classifier against adversarial manipulation","author":"hein","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref13","article-title":"Gradient masking causes clever to overestimate adversarial perturbation size","author":"goodfellow","year":"2018","journal-title":"arXiv preprint arXiv 1804 02671"},{"key":"ref14","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8r","year":"2018","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref15","article-title":"Countering adversarial images using input transformations","author":"guo","year":"2017","journal-title":"arXiv preprint arXiv 1711 03890"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140448"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref4","article-title":"Ead: elastic-net attacks to deep neural networks via adversarial examples","author":"chen","year":"2017","journal-title":"arXiv preprint arXiv 1709 04396"},{"key":"ref3","first-page":"2613","article-title":"Measuring neural net robustness with constraints","author":"bastani","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref6","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref8","article-title":"Magnet and” efficient defenses against adversarial attacks “ are not robust to adversarial examples","author":"carlini","year":"2017","journal-title":"arXiv preprint arXiv 1711 03890"},{"key":"ref7","article-title":"On the robustness of the cvpr 2018 white-box adversarial example defenses","author":"athalye","year":"2018","journal-title":"arXiv preprint arXiv 1804 02671"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref1","article-title":"Evaluating the robustness of neural networks: An extreme value theory approach","author":"weng","year":"2018","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140444"}],"event":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","location":"Anaheim, CA, USA","start":{"date-parts":[[2018,11,26]]},"end":{"date-parts":[[2018,11,29]]}},"container-title":["2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8637665\/8645964\/08646356.pdf?arnumber=8646356","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T05:23:13Z","timestamp":1598246593000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8646356\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/globalsip.2018.8646356","relation":{},"subject":[],"published":{"date-parts":[[2018,11]]}}}